{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_list  = [4, 7, 4, 0, -3, 4, 7]\n",
    "x_tuple = (4, 7, 4, 0, -3, 4, 7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute means explictly from the definition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_total = 0\n",
    "for x in x_list:\n",
    "    list_total += x\n",
    "    \n",
    "tuple_total = 0\n",
    "for t in x_tuple:\n",
    "    tuple_total += t\n",
    "    \n",
    "list_average  = list_total/len(x_list)\n",
    "tuple_average = tuple_total/len(x_tuple)\n",
    "  \n",
    "print(f'The average of x_list  = {list_average:.2f}')\n",
    "print(f'The average of x_tuple = {tuple_average:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## statistics.mean function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statistics\n",
    "\n",
    "mean_list  = statistics.mean(x_list)\n",
    "mean_tuple = statistics.mean(x_tuple)\n",
    "\n",
    "print(f'statistics.mean(x_list)  = {mean_list:.2f}')\n",
    "print(f'statistics.mean(x_tuple) = {mean_tuple:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a NumPy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "x_array = np.array(x_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## np.mean and np.ndarray.mean functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np_mean_list       = np.mean(x_list)           # operate on a list\n",
    "np_mean_tuple      = np.mean(x_tuple)          # operate on a list\n",
    "np_mean_array      = np.mean(x_array)          # operate on an array\n",
    "ndarray_mean_array = np.ndarray.mean(x_array)  # operate on an array only\n",
    "\n",
    "print(f'np.mean(x_list)  = {np_mean_list:.2f}')\n",
    "print(f'np.mean(x_tuple) = {np_mean_tuple:.2f}')\n",
    "print(f'np.mean(x_array) = {np_mean_array:.2f}')\n",
    "print(f'np.ndarray.mean(x_array) = {ndarray_mean_array:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2-d array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix = np.array([[2, 5, 6, 3],\n",
    "                   [1, 7, 0, 4],\n",
    "                   [3, 1, 2, 6]])\n",
    "\n",
    "mean_of_all  = np.mean(matrix)\n",
    "mean_of_cols = np.mean(matrix, axis=0)\n",
    "mean_of_rows = np.mean(matrix, axis=1)\n",
    "\n",
    "print(f'np.mean(matrix) [all]          = {mean_of_all}')\n",
    "print(f'np.mean(matrix, axis=0) [cols] = {mean_of_cols}')\n",
    "print(f'np.mean(matrix, axis=1) [rows] = {mean_of_rows}')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}